{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport torch\nimport torchvision.datasets as dsets\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader, TensorDataset\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\nimport random\n\ntrain = pd.read_csv('../input/digit-recognizer/train.csv')\ntest = pd.read_csv('../input/digit-recognizer/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = train.drop('label', axis=1)\nx_train = torch.FloatTensor(x_train.values)\n\ny_train = train['label']\ny_train = torch.LongTensor(y_train.values)\n\n\ndataset = TensorDataset(x_train, y_train)\ndataloader = DataLoader(dataset, batch_size=256, shuffle=True)\ntraining_epochs = 50\nmodel = nn.Linear(784, 10, bias=True)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for epoch in range(training_epochs):\n    avg_cost = 0\n    total_batch = len(dataloader)\n\n    for X, Y in dataloader:\n        optimizer.zero_grad()\n        hypothesis = model(X)\n        cost = criterion(hypothesis, Y)\n        cost.backward()\n        optimizer.step()\n\n        avg_cost += cost / total_batch\n\n    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n\nprint('Learning finished')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test = torch.FloatTensor(test.values)\nanswer = torch.argmax(model(x_test), 1)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame(answer.numpy(), columns=['Label'])\nsubmission.index = range(1,len(submission)+1)\nsubmission.to_csv(\"./submission.csv\", index_label = 'ImageId')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}